.Rhistory

table(Idents(exampleSeuratObjectUnlabelled))
createRFModel(exampleSeuratObjectLabelled)
Idents(exampleSeuratObjectLabelled)
table <- as.data.frame(as.matrix(exampleSeuratObjectLabelled$Idents))
table <- as.data.frame(as.matrix(Idents(exampleSeuratObjectLabelled)))
head(table)
library(rfca)
createRFModel(exampleSeuratObjectLabelled)
tm <- createRFModel(exampleSeuratObjectLabelled)
predictCells(exampleSeuratObjectUnlabelled, tm)
tm
predictCells(exampleSeuratObjectUnlabelled)
library(rfca)
tm <- createRFModel(exampleSeuratObjectLabelled)
tso <- createSeuratObjectPipeline()
predictCells(tso)
library(rfca)
library(Seurat)
unl <- createSeuratObjectPipeline()
rfm <- createRFModel(exampleSeuratObjectLabelled)
labelled <- predictCells(unl, rfModel = rfm)
library(rfca)
rfm <- createRFModel(exampleSeuratObjectLabelled)
rfm
predictCells(unl, rfModel = rfm[1], genelist = rfm[2])
rfm[1]
rfm[2]
length(rfm[2])
library(rfca)
createRFModel(exampleSeuratObjectLabelled)
rfm <- createRFModel(exampleSeuratObjectLabelled)
l <- varUsed(rfm)
library(randomForest)
l <- varUsed(rfm)
l
l <- varUsed(rfm, count = FALSE)
l
importance(rfm)
dim(importance(rfm))
importance(rfm)[1]
importance(rfm)[, 1]
row.names(importance(rfm))
document()
rfm
library(rfca)
predictCells(unl)
unl <- createSeuratObjectPipeline()
predictCells(unl)
rfm
predictCells(unl, rfModel= rfm)
predictCells(unl, rfm)
library(rfca)
predictCells(unl, rfm)
predictCells(unl)
git remote add origin https://github.com/kimberle9/rfca
setwd("~/Desktop/rfca")
library(devtools)
build_readme()
library(rfca)
build_readme()
document()
build_readme()
build_readme()
build_readme()
library(devtools)
document()
document()
library(rfca)
gsl <- list()
gsl$Microglia <- c("P2ry12", "Apoe")
gsl$Oligo <- c("Mbp", "Olig2", "Mobp")
gsl
p <- cellMarkerPlots(exampleSeuratObjectLabelled, gsl)
p
head(rfm)
rfm
head(importance(rfm))
head(importance(rfm)[, 1])
importance(rfm[,5])
importance(rfm)[, 1]
importance(rfm)[, 5]
sort(importance(rfm)[, 5])
sort(importance(rfm)[, 5], descending = TRUE)
sort(importance(rfm)[, 5], decreasing = TRUE)
dim(sort(importance(rfm)[, 5], decreasing = TRUE))
length(sort(importance(rfm)[, 5], decreasing = TRUE))
names((importance(rfm)[, 5], decreasing = TRUE)[1:10])
c <- names((importance(rfm)[, 5], decreasing = TRUE))
names(sort(importance(rfm)[, 5], decreasing = TRUE))[1:10]
n <- ncol(importance(rfm)) -2
n
x <-2
col.names(importance(rfModel))[x]
colnames(importance(rfModel))[x]
colnames(importance(rfm))[x]
colnames(importance(rfm))
document()
g <- createGeneLists(rfm)
g
vec <- colnames(importance(rfm))
vec <- vec[1:length(vec)-2]
l <- length(vec)-2
vec <- vec[1:l]
vec
importance(rfm)$Astrocytes
createGeneLists(rfm)
document()
createGeneLists(rfm)
g <- createGeneLists(rfm)
g
df <- importance(rfModel)
df <- importance(rfm)
cellTypeList <- which( colnames(df) == "Microglia")
cellTypeList <- names(sort(cellTypeList, decreasing = TRUE))[1:10]
cellTypeList
cellTypeList <- which( colnames(df) == "Microglia")
cellTypeList
colnames(df)
df[which( colnames(df) == "cellType=="Tcells"")]
cellTypeList <- df[, which(colnames(df) == "Microglia")]
cellTypeList
cellTypeList <- names(sort(cellTypeList, decreasing = TRUE))[1:10]
cellTypeList
document()
createGeneLists(rfm)
document()
createGeneLists(rfm)
g <- createGeneLists(rfm)
p <- cellMarkerPlots(exampleSeuratObjectLabelled, g)
g
p <- cellMarkerPlots(exampleSeuratObjectLabelled, geneList = g)
document()
p <- cellMarkerPlots(exampleSeuratObjectLabelled, g)
p
library(Seurat)
DimPlot(exampleSeuratObjectLabelled)
rm(exampleSeuratObjectUnlabelled)
DimPlot(exampleSeuratObjectUnlabelled)
rm(exampleSeuratObjectUnlabelled)
View(exampleSeuratObjectLabelled)
library(devtools)
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
build_readme()
library(Seurat)
?FeaturePlot
document()
p <- cellMarkerPlots(exampleSeuratObjectUnlabelled)
p
p <- cellMarkerPlots(exampleSeuratObjectUnlabelled, g)
p
document()
build_readme()
oligosonly <- subset(x = exampleSeuratObjectLabelled, idents = "Oligodendrocytes")
head(oligosonly[[]])
oligosonly <- FindVariableFeatures(oligosonly, selection.method = "vst")
all.genes <- rownames(oligosonly)
seurat.obj <- ScaleData(oligosonly, features = all.genes)
seurat.obj <- RunPCA(seurat.obj, features = VariableFeatures(object = seurat.obj))
oligosonly <- ScaleData(oligosonly, features = all.genes)
oligosonly <- RunPCA(oligosonly, features = VariableFeatures(object = oligosonly))
ElbowPlot(oligosonly)
oligosonly<- FindNeighbors(oligosonly, dims = 1:14)
oligosonly <- FindClusters(oligosonly, resolution = 0.6)
oligosonly <- RunUMAP(oligosonly, dims = 1:14)
DimPlot(oligosonly)
oligosonly
FeaturePlot(oligosonly, c("C4b", "Serpina3n"))
FeaturePlot(oligosonly, "Klk6")
build_readme()
View(exampleSeuratObject)
build_readme()
?build_manual
build_manual()
use_vignette("vignette")
table(Idents(exampleSeuratObjectLabelled))
exampleSeuratObjectLabelled
build_readme()
myList <- list()
clear()
myList$Microglia <- c("Cd11b", "Apoe")
myList$Oligodendrocytes <- c("Mbp", "Mobp", "Cldn11"
)
myList
build_readme()
install.packages("pkgdown")
library(pkgdown)
usethis::use_pkgdown()
pkgdown::build_site()
usethis::use_github_action("pkgdown")
pkgdown::build_site()
build_readme()
pkgdown::build_site()
tso
mySeuratObject <-tso
library(rfca)
predictCells(mySeuratObject)
rfm
myRandomForestModel <- rfm
predictCells(mySeuratObject)
predictCells(mySeuratObject, myRandomForestModel)
build_readme()
build_readme()
document*()
document()
build_readme()
library(Seurat)
pbmc <- readRDS(file = "../data/pbmc3k_final.rds")
setwd("~/Desktop")
pbmc.data <- Read10X(data.dir = "../hg19/")
pbmc.data <- Read10X(data.dir = "hg19")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- NormalizeData(pbmc)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
pbmc <- RunUMAP(pbmc, dims = 1:10)
DimPlot(pbmc, reduction = "umap")
new.cluster.ids <- c("Naive CD4 T", "Memory CD4 T", "CD14+ Mono", "B", "CD8 T", "FCGR3A+ Mono",
"NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
library(randomForest)
pbmc
library(rfca)
humanPBMC <- createRFModel(pbmc)
head(pbmc[[]])
Idents(pbmc) <- seurat_clusters
Idents(pbmc) <- pbmc$seurat_clusters
new.cluster.ids <- c("Tcells", "Tcells", "Monocytes", "Bcells", "Tcells", "Monocytes", "NKcells", "dendriticCells", "Platelets")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
humanPBMC <- createRFModel(pbmc)
mouseBrain <- readRDS("rfmodel.RDS")
mouseBrain
humanPBMC
t <- createSeuratObjectPipeline()
FeaturePlot(t, c("Ms4a1", "Ncr1", "Cd3e"))
FeaturePlot(t, c("Col1a1", "Pdgfrb", "Pdgfra", "Plp1"))
FeaturePlot(t, c("Cd209a", "Fcgr1", "Cdh5", "Acta2"))
FeaturePlot(t, c("Mylk", "Sncg", "Des", "Tagln"))
FeaturePlot(t, c("Mylk", "Sncg", "Des", "Ly6c1"))
FeaturePlot(t, c("Wif1", "Tbx20", "Dkk3", "Lamc1"))
FeaturePlot(t, c("Wif1", "Tbx20", "Mrc1", "Dab2"))
FeaturePlot(t, c("Wif1", "Myh11", "Kcnj8", "Vtn"))
DimPlot(t)
a <- t
a.markers <- FindAllMarkers(a, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
a.markers %>% group_by(cluster) %>% top_n(n = 3, wt = avg_logFC) %>% print(n = 100)
library(magrittr)
library(dplyr)
a.markers %>% group_by(cluster) %>% top_n(n = 3, wt = avg_logFC) %>% print(n = 100)
t <- dget("droplet_Heart_and_Aorta_seurat_tiss.Robj")
t <- dget("droplet_Heart_and_Aorta_seurat_tiss.robj")
t = dget("droplet_Heart_and_Aorta_seurat_tiss.robj")
t <- dget("https://ndownloader.figshare.com/files/13088642")
t <- load("droplet_Heart_and_Aorta_seurat_tiss.robj")
t
tiss
head(tiss[[]])
Idents(tiss)
t <- UpdateSeuratObject(tiss)
t
head(t[[]])
table(t$cell_ontology_class)
mouseBrain
humanPBMC
l <- load("droplet_Liver_seurat_tiss.Robj")
l <- UpdateSeuratObject(l)
l
l <- UpdateSeuratObject(tiss)
head(l[[]])
table(l$cell_ontology_class)
table(l$subtissue)
Idents(l) <- l$cell_ontology_class
mouseLiver <- createRFModel(l)
mouseLiver <- createRFModel(l)
View(mouseLiver)
l <- load("facs_Liver_seurat_tiss.Robj")
l
l <- UpdateSeuratObject(tiss)
head(l[[]])
table(l$cell_ontology_class)
library(rfca)
library(Seurat)
library(randomForest)
l
l <- load("facs_Liver_seurat_tiss.Robj")
l1 <- UpdateSeuratObject(tiss)
l <- load("droplet_Liver_seurat_tiss.Robj")
l2 <- UpdateSeuratObject(tiss)
l <- merge(l1, y = l2)
l
head(l[[]])
table(l$cell_ontology_class)
Idents(l) <- l$cell_ontology_class
mouseLiver <- createRFModel(l)
k1 <- load("droplet_Kidney_seurat_tiss.Robj")
k1
k1 <- UpdateSeuratObject(tiss)
k2 <- load("facs_Kidney_seurat_tiss.Robj")
k1
k2 <- UpdateSeuratObject(tiss)
ks
k2
k <- merge(k1, y= k2)
k
mouseKidney <- createRFModel(k)
k
Idents(k) <- k$cell_ontology_class
Idents(k)
mouseKidney <- createRFModel(k)
f <- load("facs_Fat_seurat_tiss.Robj")
f <- UpdateSeuratObject(tiss)
table(f$cell_ontology_class)
Idents(f) <- f$cell_ontology_class
f
mouseFat <- createRFModel(f)
Idents(f)
mouseFat <- createRFModel(f, nfeatures = 100)
table(f$cell_ontology_class)
table(Idents(f))
mouseKidney
mouseBrain
humanPBMC
saveRDS(mouseLiver, "mouseLiver.RDS")
saveRDS(mouseKidney, "mouseKidney.RDS")
mouseFat <- createRFModel(f)
saveRDS(mouseFat, "mouseFat.RDS")
h1 <- load("facs_Heart_seurat_tiss.Robj")
h1 <- UpdateSeuratObject(tiss)
h2 <- load("droplet_Heart_and_Aorta_seurat_tiss.Robj")
h2 <- UpdateSeuratObject(tiss)
h <- merge(h1, y=h2)
Idents(h) <- h$cell_ontology_class
table(Idents(h))
View(k)
mouseHeart <- createRFModel(h)
m1 <- load("droplet_Limb_Muscle_seurat.tiss.Robj")
m1<- load("droplet_Limb_Muscle_seurat_tiss.Robj")
m1 <- UpdateSeuratObject(tiss)
m2 <- load("facs_Limb_Muscle_seurat_tiss.Robj")
m2 <- UpdateSeuratObject(tiss)
m <- merge(m1, y=m2)
m
Idents(m) <- m$cell_ontology_class
table(Idents(m))
mouseMuscle <- createRFModel(m)
saveRDS(mouseFat, "mouseFat.RDS")
saveRDS(mouseMuscle, "mouseMuscle.RDS")
saveRDS(mouseHeart, "mouseHeart.RDS")
s <- load("facs_Skin_seurat_tiss.Robj")
s <- UpdateSeuratObject(tiss)
Idents(s) <- s$cell_ontology_class
table(Idents(s))
mouseSkin <- createRFModel(s)
saveRDS(mouseSkin, "mouseSkin.RDS")
library(devtools)
mouseBrain <- readRDS("rfmodel.RDS")
mouseBrain
setwd("~/Desktop/rfca")
use_data(mouseBrain, mouseMuscle, mouseKidney, mouseLiver, mouseHeart)
use_date(mouseBrain, mouseMuscle, mouseKidney, mouseLiver, mouseHeart, internal = TRUE)
use_data(mouseBrain, mouseMuscle, mouseKidney, mouseHeart, mouseLiver, internal = TRUE)
document()
R_MAX_VSIZE
rm(mouseFat)
rm(mouseSkin)
rm(f, m, m1, m2, l, l1, l2, k, k1, k2, tiss)
rm(h, h1, h2)
document()
rm(list = c("mouseBrain", "mouseHeart", "mouseKidney", "mouseLiver", "mouseMuscle"))
rm(s)
document()
build_readme()
library(pkgdown)
build_site()
build_readme()
cellMarkerPlots <- function(seuratObj, geneList = gsl, pt.size = 0.3, label = TRUE, label.size = 1, repel = FALSE, combine = TRUE){
for (name in names(geneList)) {
log2_norm_umi <- GetAssayData(seuratObj)
log2_norm_umi_selected <- log2_norm_umi[rownames(log2_norm_umi) %in% geneList[[name]], ]
averageScore <- colMeans(as.array(log2_norm_umi_selected))
seuratObj<- AddMetaData(seuratObj, col.name = name, metadata = averageScore)
print(paste0("Calculating average expression of genes in ", name, " list."))
}
plottingInputs <- c(pt.size, label, label.size, repel, combine)
fPlot <- FeaturePlot(seuratObj, names(geneList), pt.size = plottinInputs[1], label = plottingInputs[2], label.size = plottingInputs[3], repel = plottingInputs[4], combine = plottingInputs[5])
return(fPlot)
}
myRandomForestModel <- createRFModel(exampleSeuratObjectLabelled)
markerGeneList <- createGeneLists(myRandomForestModel)
myPlot <- cellMarkerPlots(exampleSeuratObjectLabelled, geneList = markerGeneList)
cellMarkerPlots <- function(seuratObj, geneList = gsl, pt.size = 0.3, label = TRUE, label.size = 1, repel = FALSE, combine = TRUE){
for (name in names(geneList)) {
log2_norm_umi <- GetAssayData(seuratObj)
log2_norm_umi_selected <- log2_norm_umi[rownames(log2_norm_umi) %in% geneList[[name]], ]
averageScore <- colMeans(as.array(log2_norm_umi_selected))
seuratObj<- AddMetaData(seuratObj, col.name = name, metadata = averageScore)
print(paste0("Calculating average expression of genes in ", name, " list."))
}
plottingInputs <- c(pt.size, label, label.size, repel, combine)
fPlot <- FeaturePlot(seuratObj, names(geneList), pt.size = plottingInputs[1], label = plottingInputs[2], label.size = plottingInputs[3], repel = plottingInputs[4], combine = plottingInputs[5])
return(fPlot)
}
myPlot <- cellMarkerPlots(exampleSeuratObjectLabelled, geneList = markerGeneList)
myPlot
cellMarkerPlots <- function(seuratObj, geneList = gsl, pt.size = 0.3, label = TRUE, label.size = 3, repel = FALSE, combine = TRUE){
for (name in names(geneList)) {
log2_norm_umi <- GetAssayData(seuratObj)
log2_norm_umi_selected <- log2_norm_umi[rownames(log2_norm_umi) %in% geneList[[name]], ]
averageScore <- colMeans(as.array(log2_norm_umi_selected))
seuratObj<- AddMetaData(seuratObj, col.name = name, metadata = averageScore)
print(paste0("Calculating average expression of genes in ", name, " list."))
}
plottingInputs <- c(pt.size, label, label.size, repel, combine)
fPlot <- FeaturePlot(seuratObj, names(geneList), pt.size = plottingInputs[1], label = plottingInputs[2], label.size = plottingInputs[3], repel = plottingInputs[4], combine = plottingInputs[5])
return(fPlot)
}
myPlot <- cellMarkerPlots(exampleSeuratObjectLabelled, geneList = markerGeneList)
myPlot
autoLabelledSeuratObject <- predictCells(exampleSeuratObjectUnlabelled, myRandomForestModel)
document()
rm(list = c("cellMarkerPlots"))
document()
build_readme()
.Last.error.trace
document()
mouseBrain <- readRDS("mouseBrain.RDS")
setwd("~/Desktop")
mouseBrain <- readRDS('mouseBrain.RDS')
mouseBrain <- readRDS("rfmodel.RDS")
mouseLiver <- readRDS("mouseLiver.RDS")
mouseKidney <- readRDS("mouseKidney.RDS")
use_data(mouseBrain, mouseKidney, mouseLiver, internal = TRUE)
rm(c('mouseBrain', 'mouseKidney', 'mouseLiver'))
rm("mouseBrain")
rm("mouseLiver")
rm("mouseKidney")
rm("myRandomForestModel")
rm(myPlot)
rm(autoLabelledSeuratObject)
rm(markerGeneList)
mouseBrain <- readRDS('rfmodel.RDS')
use_data(mouseBrain, internal = TRUE)
mouseLiver <- readRDS("mouseLiver.RDS")
use_data(mouseBrain, mouseLiver, internal = TRUE)
library(Seurat)
pbmc.data <- Read10X(data.dir = "hg19")
pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
pbmc
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
pbmc <- NormalizeData(pbmc)
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)
pbmc <- RunUMAP(pbmc, dims = 1:10)
new.cluster.ids <- c("Tcells", "Tcells", "Monocytes", "Bcells", "Tcells", "Monocytes",
"NKcells", "DC", "Platelets")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()
library(rfca)
humanPBMC <- createRFModel(pbmc)
saveRDS(humanPBMC, "humanPBMC.RDS")
mouseMuscle <- readRDS("mouseMuscle.RDS")
use_data(mouseBrain, humanPBMC, mouseLiver, mouseMuscle, internal = TRUE)
features <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = nfeatures)
features <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 200)
topfeatures <- VariableFeatures(features)
topfeaturesedited <- make.names(topfeatures)
topfeaturescleaned <- intersect(topfeatures, topfeaturesedited)
matrix <- as.matrix(GetAssayData(pbmc, slot = "counts"))
m <- subset(matrix, rownames(matrix) %in% topfeaturescleaned)
m <- t(m)
table <- as.data.frame(as.matrix(Idents(pbmc)))
merged <- merge(m, table, by = "row.names", all = TRUE)
merged$V1 <- as.factor(merged$V1)
merged$"Row.names" <- merged$"row.names" <- NULL
rfmodel <- randomForest(V1 ~ ., data=merged, importance=TRUE, proximity=TRUE, ntree = 300)
rfmodel
rfmodel <- randomForest(V1 ~ ., data=merged, importance=TRUE, proximity=TRUE, ntree = 250)
rfmodel
rfmodel <- randomForest(V1 ~ ., data=merged, importance=TRUE, proximity=TRUE, ntree = 200)
rfmodel
saveRDS(rfmodel, "humanPBMC2.RDS")
library(Seurat)